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Social Change and Socioeconomic Disparities in Health over the Life Course in China: A Cohort Analysis

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TLDR
This article examines social stratification in individual health trajectories for multiple cohorts in the context of China's dramatically changing macro-social environment using data from the China Health and Nutrition Survey and finds significant socioeconomic status (SES) differences in the mean level of health.
Abstract
This article examines social stratification in individual health trajectories for multiple cohorts in the context of China's dramatically changing macro-social environment. Using data from the China Health and Nutrition Survey, we find significant socioeconomic status (SES) differences in the mean level of health and that these SES differentials generally diverge over the life course. We also find strong cohort variations in SES disparities in the mean levels of health and health trajectories. The effect of education on health slightly decreases across successive cohorts. By contrast, the income gap in health trajectories diverges for earlier cohorts but converges for most recent cohorts. Both effects are more pronounced in rural areas. Given that these cohort effects are opposite those reported in recent U.S. studies, we discuss China's unique social, economic, and political settings. We highlight the association between SES and health behaviors, China's stage of epidemiologic transition, and the changing power of the state government and its implications for health care.

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Reference EntryDOI

Human Development in Time and Place

TL;DR: The study of the life course and human development has evolved rapidly over the past quarter century, extending across substantive boundaries of the social, behavioral, and medical sciences as discussed by the authors, and these cutting-edge studies tell us much about the dimensions of historical time and place and their importance in shaping biographical pathways.
References
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Book

Hierarchical Linear Models: Applications and Data Analysis Methods

TL;DR: The Logic of Hierarchical Linear Models (LMLM) as discussed by the authors is a general framework for estimating and hypothesis testing for hierarchical linear models, and it has been used in many applications.
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Hierarchical Linear Models: Applications and Data Analysis Methods.

TL;DR: This chapter discusses Hierarchical Linear Models in Applications, Applications in Organizational Research, and Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known.
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Applied Longitudinal Data Analysis

TL;DR: In this paper, a framework for investigating change over time is presented, where the multilevel model for change is introduced and a framework is presented for investigating event occurrence over time.
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Self-rated health and mortality : a review of twenty-seven community studies

TL;DR: This work examines the growing number of studies of survey respondents' global self-ratings of health as predictors of mortality in longitudinal studies of representative community samples and suggests several approaches to the next stage of research in this field.
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The Matthew effect in science. The reward and communication systems of science are considered.

TL;DR: The psychosocial conditions and mechanisms underlying the Matthew effect are examined and a correlation between the redundancy function of multiple discoveries and the focalizing function of eminent men of science is found—a function which is reinforced by the great value these men place upon finding basic problems and by their self-assurance.
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